PET 3-DIMENSIONAL DATA QUANTIFICATION

PET 3 维数据量化

• 批准号: 2390882
• 负责人: TERRENCE R OAKES
• 金额: $ 2.56万
• 依托单位: UNIVERSITY OF BRITISH COLUMBIA
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-10 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/2390882
• 关键词: data collection methodology /evaluation; diagnosis quality /standard; image processing; positron emission tomography

The primary goal of the proposed research is to quantitate PET data acquired in 3-D-mode. The outstanding feature of PET is that it enables researchers and clinicians to assign a quantitative value or rate to an in vivo physiological process. These processes include such things as glucose utilization by tumors, blood supply to the heart, or-the ability of the brain to use neurotransmitters. By removing the collimating septa and acquiring PET data in 3D-mode, the sensitivity is increased by a factor of -6, compared to the conventional slice-mode. If 3D PET could be used on a quantitative basis, the 6-fold increase in sensitivity would translate directly to either a 6-fold decrease in patient dose, a 6-fold increase in statistics, or a 6-fold increase in patient throughput. While data can be acquired in 3D-mode today, it is not yet possible to assign a quantitative value to this data. The difficulties in quantitating 3D PET stem from the lack of collimation, which permits a greater number of undesirable scattered gama-rays to be detected. A tradeoff is made: more "true" events are detected, but more scattered events are also detected, which makes quantitation more difficult. The research proposed here seeks to develop methods and algorithms to enable 3D PET quantitation. The specific areas to be addressed include: detector calibration (assigning an accurate muCi/ml tissue to each image pixel); detector normalization (correcting for differing sensitivities of individual detectors); scatter correction: (correcting for the undesirable scattered events); attenuation correction (since radioactive events originating deep in a tissue mass will be scattered more than those from a superficial location); and image reconstruction (transforming sinogram data into a recognizable image).

拟议研究的主要目标是对PET数据进行量化 以3-D模式获取。PET的突出特点是它能够 研究人员和临床医生将量化的值或比率分配给 在体内的生理过程。这些流程包括以下内容 肿瘤对葡萄糖的利用，心脏的血液供应，或-能力 使用神经递质的大脑。通过移除准直隔膜 并以3D模式采集PET数据，使灵敏度提高了 与传统的切片模式相比，倍数为-6。如果3D PET可以 在定量的基础上使用，灵敏度增加6倍将 直接转化为患者剂量减少6倍、6倍 统计数据增加，或患者吞吐量增加6倍。而当 今天可以在3D模式下获取数据，目前还不能分配 该数据的量化值。3D定量化的难点 宠物起源于缺乏准直，这允许更多的 不希望探测到的散布的伽马射线。做了一个权衡：更多 可以检测到“真”事件，但也可以检测到更分散的事件， 这使得量化变得更加困难。 这里提出的研究试图开发方法和算法来 启用3D PET量化。需要解决的具体领域包括： 探测器校准(为每个图像分配精确的Muci/ml组织 像素)；探测器归一化(校正不同的灵敏度 单个探测器)；散射校正：(校正 不受欢迎的分散事件)；衰减校正(自放射性起 起源于组织块深处的事件将比 来自表面位置的图像)；以及图像重建 (将正弦图数据转换为可识别的图像)。